What I Am Doing to Stay Relevant as a Senior Analytics Consultant in 2026
Learn how to work with AI, while strengthening your unique human skills that technology cannot replace
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Pydantic Performance: 4 Tips on How to Validate Large Amounts of Data Efficiently
The real value lies in writing clearer code and using your tools right
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TDS Newsletter: Vibe Coding Is Great. Until It’s Not.
Sorting through the good, bad, and ambiguous aspects of vibe coding
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Why Is My Code So Slow? A Guide to Py-Spy Python Profiling
Stop guessing and start diagnosing performance issues using Py-Spy
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The Rule Everyone Misses: How to Stop Confusing loc and iloc in Pandas
A simple mental model to remember when each one works (with examples that finally click).
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AWS vs. Azure: A Deep Dive into Model Training – Part 2
This article covers how Azure ML's persistent, workspace-centric compute resources differ from AWS SageMaker's on-demand, job-specific approach. Additionally, we explored environment customization options, from Azure's curated environments and custom environments to SageMaker's three level of custom...
How to Work Effectively with Frontend and Backend Code
Learn how to be an effective full-stack engineer with Claude Code
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How to Build Your Own Custom LLM Memory Layer from Scratch
Step-by-step guide to building autonomous memory retrieval systems
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YOLOv2 & YOLO9000 Paper Walkthrough: Better, Faster, Stronger
From YOLOv1 to YOLOv2: prior box, k-means, Darknet-19, passthrough layer, and more
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Creating a Data Pipeline to Monitor Local Crime Trends
A walkthough of creating an ETL pipeline to extract local crime data and visualize it in Metabase.
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Silicon Darwinism: Why Scarcity Is the Source of True Intelligence
We are confusing “size” with “smart.” The next leap in artificial intelligence will not come from a larger data center, but from a more constrained environment.
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Distributed Reinforcement Learning for Scalable High-Performance Policy Optimization
Leveraging massive parallelism, asynchronous updates, and multi-machine training to match and exceed human-level performance
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Learn how to efficiently solve problems with coding agents
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How to Run Claude Code for Free with Local and Cloud Models from Ollama
Ollama now offers Anthropic API compatibility
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Why Your Multi-Agent System is Failing: Escaping the 17x Error Trap of the “Bag of Agents”
Hard-won lessons on how to scale agentic systems without scaling the chaos, including a taxonomy of core agent types.
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How to structure decisions, identify efficient options, and avoid misleading value metrics
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Optimizing Vector Search: Why You Should Flatten Structured Data
An analysis of how flattening structured data can boost precision and recall by up to 20%
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Randomization Works in Experiments, Even Without Balance
Randomization usually balances confounders in experiments, but what happens when it doesn't?
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Data Science as Engineering: Foundations, Education, and Professional Identity
Recognize data science as an engineering practice and structure education accordingly.
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